The near infrared spectrum(NIRS) technology is used to detect the fat content of oat.With 93 oat collected from China as samples,the model of the artificial neural network for determining fat content in oat is established after preprocessing the near infrared spectrum data and extracting the spectral characteristics by principle component analysis (PCA).It is shown that the preprocessing of spectrum scattering is the inverse multiple scatter correction (IMSC) and mathematics processing is 2441(2 is the second derivative processing; 4 is the interval point of the second derivative; 4 is the first smoothing interval point and 1 is no secondary smoothing).The structure of the artificial neural network mode is 2-17-1,which is established after extracting 2 principle component as the characteristic variables of the original information. The correlation coefficient of the true value and the prediction value is 0.962 3,and the root mean square deviation is 1.607 2.The model has better predictive accuracy and can be used to detect fat content in oat rapidly.

BP neural network; fat content; near infrared spectrum; oat; principle component analysis; spectrum preprocessing